Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations10000
Missing cells94314
Missing cells (%)31.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory232.0 B

Variable types

Numeric18
Categorical11
Unsupported1

Alerts

cozinhas has constant value "1.0" Constant
varandas has constant value "1.0" Constant
sacadas has constant value "1.0" Constant
terracos has constant value "1.0" Constant
areas_servico has constant value "1.0" Constant
dce has constant value "1.0" Constant
churrasqueiras has constant value "1.0" Constant
wc has constant value "1.0" Constant
wc_emp has constant value "1.0" Constant
area_privativa is highly overall correlated with lavabosHigh correlation
area_terreno is highly overall correlated with lavabos and 1 other fieldsHigh correlation
area_total is highly overall correlated with m2totalHigh correlation
desconto is highly overall correlated with preco and 4 other fieldsHigh correlation
lavabos is highly overall correlated with area_privativa and 7 other fieldsHigh correlation
m2privativa is highly overall correlated with preco and 4 other fieldsHigh correlation
m2terreno is highly overall correlated with area_terreno and 1 other fieldsHigh correlation
m2total is highly overall correlated with area_totalHigh correlation
n_lances is highly overall correlated with lavabosHigh correlation
preco is highly overall correlated with desconto and 6 other fieldsHigh correlation
valor_de_avaliacao is highly overall correlated with desconto and 5 other fieldsHigh correlation
valor_oferta is highly overall correlated with desconto and 6 other fieldsHigh correlation
valor_presente is highly overall correlated with desconto and 6 other fieldsHigh correlation
valor_venda is highly overall correlated with desconto and 6 other fieldsHigh correlation
salas is highly imbalanced (92.7%) Imbalance
lavabos is highly imbalanced (89.4%) Imbalance
quartos has 1529 (15.3%) missing values Missing
salas has 1730 (17.3%) missing values Missing
vagas_garagem has 3403 (34.0%) missing values Missing
lavabos has 9773 (97.7%) missing values Missing
suites has 10000 (100.0%) missing values Missing
cozinhas has 1684 (16.8%) missing values Missing
varandas has 9483 (94.8%) missing values Missing
sacadas has 9824 (98.2%) missing values Missing
terracos has 9751 (97.5%) missing values Missing
areas_servico has 5593 (55.9%) missing values Missing
dce has 9905 (99.1%) missing values Missing
churrasqueiras has 9998 (> 99.9%) missing values Missing
wc has 1811 (18.1%) missing values Missing
wc_emp has 9830 (98.3%) missing values Missing
classificacao is highly skewed (γ1 = 23.6817897) Skewed
n_lances is highly skewed (γ1 = 23.34055607) Skewed
area_terreno is highly skewed (γ1 = 30.20264893) Skewed
m2terreno is highly skewed (γ1 = 42.336763) Skewed
suites is an unsupported type, check if it needs cleaning or further analysis Unsupported
perc_de_luta has 3289 (32.9%) zeros Zeros
area_total has 6295 (62.9%) zeros Zeros
area_privativa has 579 (5.8%) zeros Zeros
area_terreno has 6413 (64.1%) zeros Zeros
m2total has 6295 (62.9%) zeros Zeros
m2privativa has 579 (5.8%) zeros Zeros
m2terreno has 6413 (64.1%) zeros Zeros

Reproduction

Analysis started2025-12-18 19:46:24.647843
Analysis finished2025-12-18 19:46:49.266345
Duration24.62 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

classificacao
Real number (ℝ)

Skewed 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0116
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-18T16:46:49.325879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.198165
Coefficient of variation (CV)0.19589264
Kurtosis658.65859
Mean1.0116
Median Absolute Deviation (MAD)0
Skewness23.68179
Sum10116
Variance0.039269367
MonotonicityNot monotonic
2025-12-18T16:46:49.401284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 9945
99.5%
2 30
 
0.3%
3 10
 
0.1%
4 7
 
0.1%
6 3
 
< 0.1%
8 2
 
< 0.1%
7 2
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
1 9945
99.5%
2 30
 
0.3%
3 10
 
0.1%
4 7
 
0.1%
5 1
 
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 2
 
< 0.1%
6 3
 
< 0.1%
5 1
 
< 0.1%
4 7
 
0.1%
3 10
 
0.1%
2 30
 
0.3%
1 9945
99.5%

valor_venda
Real number (ℝ)

High correlation 

Distinct3241
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119908.7
Minimum2472.92
Maximum2040043.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-18T16:46:49.447828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2472.92
5-th percentile50984.319
Q179804.913
median97778.11
Q3126514.08
95-th percentile245366.01
Maximum2040043.1
Range2037570.2
Interquartile range (IQR)46709.167

Descriptive statistics

Standard deviation105469.62
Coefficient of variation (CV)0.87958272
Kurtosis85.442831
Mean119908.7
Median Absolute Deviation (MAD)21761.975
Skewness7.4323079
Sum1.199087 × 109
Variance1.1123841 × 1010
MonotonicityNot monotonic
2025-12-18T16:46:49.731896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87819.45 142
 
1.4%
106667.03 121
 
1.2%
100741.08 120
 
1.2%
94815.14 117
 
1.2%
81964.82 105
 
1.1%
97778.11 87
 
0.9%
84892.14 78
 
0.8%
90746.77 76
 
0.8%
78000 68
 
0.7%
114838.57 67
 
0.7%
Other values (3231) 9019
90.2%
ValueCountFrequency (%)
2472.92 1
 
< 0.1%
3228.86 1
 
< 0.1%
3369.5 1
 
< 0.1%
3516 1
 
< 0.1%
3820.72 7
0.1%
4102 1
 
< 0.1%
4453.6 1
 
< 0.1%
4688 1
 
< 0.1%
4729.02 1
 
< 0.1%
4834.5 1
 
< 0.1%
ValueCountFrequency (%)
2040043.07 1
< 0.1%
2014957.2 1
< 0.1%
1889571.69 1
< 0.1%
1784480.04 1
< 0.1%
1666873.34 1
< 0.1%
1657017.18 1
< 0.1%
1642532.16 1
< 0.1%
1610360.84 1
< 0.1%
1520000 1
< 0.1%
1485800 1
< 0.1%

valor_oferta
Real number (ℝ)

High correlation 

Distinct6600
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137818.17
Minimum5370
Maximum3026000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-18T16:46:49.806891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5370
5-th percentile58080.62
Q189354.385
median110648.52
Q3145730.35
95-th percentile289048.9
Maximum3026000
Range3020630
Interquartile range (IQR)56375.963

Descriptive statistics

Standard deviation126471.08
Coefficient of variation (CV)0.91766624
Kurtosis124.33897
Mean137818.17
Median Absolute Deviation (MAD)25648.305
Skewness8.6015362
Sum1.3781817 × 109
Variance1.5994934 × 1010
MonotonicityNot monotonic
2025-12-18T16:46:49.883049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87819.45 48
 
0.5%
94815.14 47
 
0.5%
97778.11 45
 
0.4%
106667.03 43
 
0.4%
96000 31
 
0.3%
102000 30
 
0.3%
100741.08 29
 
0.3%
87000 28
 
0.3%
114838.57 27
 
0.3%
81964.82 25
 
0.2%
Other values (6590) 9647
96.5%
ValueCountFrequency (%)
5370 1
< 0.1%
10001.5 2
< 0.1%
10453.6 1
< 0.1%
10456.39 1
< 0.1%
10472.92 1
< 0.1%
11820.72 1
< 0.1%
11821 1
< 0.1%
11920 1
< 0.1%
12151.26 1
< 0.1%
12489.58 1
< 0.1%
ValueCountFrequency (%)
3026000 1
< 0.1%
2904000 1
< 0.1%
2771575 1
< 0.1%
2540000 1
< 0.1%
2040043.07 1
< 0.1%
1940360.84 1
< 0.1%
1922039.32 1
< 0.1%
1705800 1
< 0.1%
1687533 1
< 0.1%
1679722.18 1
< 0.1%

valor_presente
Real number (ℝ)

High correlation 

Distinct6601
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137810.07
Minimum5370
Maximum3026000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-18T16:46:49.930434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5370
5-th percentile58080.62
Q189279.592
median110648.52
Q3145707.55
95-th percentile289048.9
Maximum3026000
Range3020630
Interquartile range (IQR)56427.955

Descriptive statistics

Standard deviation126470.4
Coefficient of variation (CV)0.91771521
Kurtosis124.34392
Mean137810.07
Median Absolute Deviation (MAD)25646.31
Skewness8.6018492
Sum1.3781007 × 109
Variance1.5994761 × 1010
MonotonicityNot monotonic
2025-12-18T16:46:50.009721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87819.45 48
 
0.5%
94815.14 47
 
0.5%
97778.11 45
 
0.4%
106667.03 43
 
0.4%
96000 31
 
0.3%
102000 30
 
0.3%
100741.08 29
 
0.3%
87000 28
 
0.3%
114838.57 27
 
0.3%
81964.82 25
 
0.2%
Other values (6591) 9647
96.5%
ValueCountFrequency (%)
5370 1
< 0.1%
10001.5 2
< 0.1%
10453.6 1
< 0.1%
10456.39 1
< 0.1%
10472.92 1
< 0.1%
11820.72 1
< 0.1%
11821 1
< 0.1%
11920 1
< 0.1%
12151.26 1
< 0.1%
12489.58 1
< 0.1%
ValueCountFrequency (%)
3026000 1
< 0.1%
2904000 1
< 0.1%
2771575 1
< 0.1%
2540000 1
< 0.1%
2040043.07 1
< 0.1%
1940360.84 1
< 0.1%
1922039.32 1
< 0.1%
1705800 1
< 0.1%
1687533 1
< 0.1%
1679722.18 1
< 0.1%

row_id
Real number (ℝ)

Distinct610
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228.3047
Minimum3
Maximum798
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-12-18T16:46:50.057120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile24.95
Q1107
median216
Q3338
95-th percentile465
Maximum798
Range795
Interquartile range (IQR)231

Descriptive statistics

Standard deviation142.32367
Coefficient of variation (CV)0.62339352
Kurtosis-0.5103863
Mean228.3047
Median Absolute Deviation (MAD)114
Skewness0.38671486
Sum2283047
Variance20256.027
MonotonicityNot monotonic
2025-12-18T16:46:50.105661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 92
 
0.9%
47 91
 
0.9%
19 91
 
0.9%
35 91
 
0.9%
55 91
 
0.9%
51 90
 
0.9%
43 89
 
0.9%
31 89
 
0.9%
107 88
 
0.9%
139 88
 
0.9%
Other values (600) 9100
91.0%
ValueCountFrequency (%)
3 69
0.7%
7 80
0.8%
8 1
 
< 0.1%
10 1
 
< 0.1%
11 73
0.7%
12 2
 
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
15 87
0.9%
16 2
 
< 0.1%
ValueCountFrequency (%)
798 1
< 0.1%
794 1
< 0.1%
790 1
< 0.1%
786 1
< 0.1%
782 1
< 0.1%
778 1
< 0.1%
770 1
< 0.1%
766 1
< 0.1%
762 1
< 0.1%
758 1
< 0.1%

perc_de_luta
Real number (ℝ)

Zeros 

Distinct186
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.9241
Minimum0
Maximum1007
Zeros3289
Zeros (%)32.9%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-18T16:46:50.164217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q324
95-th percentile57
Maximum1007
Range1007
Interquartile range (IQR)24

Descriptive statistics

Standard deviation33.018797
Coefficient of variation (CV)1.9509928
Kurtosis246.5158
Mean16.9241
Median Absolute Deviation (MAD)8
Skewness11.7594
Sum169241
Variance1090.241
MonotonicityNot monotonic
2025-12-18T16:46:50.200380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3289
32.9%
1 321
 
3.2%
2 297
 
3.0%
10 232
 
2.3%
3 222
 
2.2%
4 220
 
2.2%
6 189
 
1.9%
5 186
 
1.9%
7 177
 
1.8%
8 175
 
1.8%
Other values (176) 4692
46.9%
ValueCountFrequency (%)
0 3289
32.9%
1 321
 
3.2%
2 297
 
3.0%
3 222
 
2.2%
4 220
 
2.2%
5 186
 
1.9%
6 189
 
1.9%
7 177
 
1.8%
8 175
 
1.8%
9 159
 
1.6%
ValueCountFrequency (%)
1007 1
< 0.1%
940 1
< 0.1%
762 1
< 0.1%
719 1
< 0.1%
616 1
< 0.1%
587 1
< 0.1%
545 1
< 0.1%
527 1
< 0.1%
512 1
< 0.1%
490 1
< 0.1%

n_lances
Real number (ℝ)

High correlation  Skewed 

Distinct96
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4449
Minimum1
Maximum1781
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-12-18T16:46:50.263543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile23
Maximum1781
Range1780
Interquartile range (IQR)4

Descriptive statistics

Standard deviation43.469087
Coefficient of variation (CV)5.8387738
Kurtosis651.34563
Mean7.4449
Median Absolute Deviation (MAD)1
Skewness23.340556
Sum74449
Variance1889.5615
MonotonicityNot monotonic
2025-12-18T16:46:50.311004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4300
43.0%
2 1547
 
15.5%
3 913
 
9.1%
4 646
 
6.5%
5 406
 
4.1%
6 314
 
3.1%
7 232
 
2.3%
8 179
 
1.8%
9 143
 
1.4%
10 123
 
1.2%
Other values (86) 1197
 
12.0%
ValueCountFrequency (%)
1 4300
43.0%
2 1547
 
15.5%
3 913
 
9.1%
4 646
 
6.5%
5 406
 
4.1%
6 314
 
3.1%
7 232
 
2.3%
8 179
 
1.8%
9 143
 
1.4%
10 123
 
1.2%
ValueCountFrequency (%)
1781 1
 
< 0.1%
1164 3
< 0.1%
985 3
< 0.1%
980 5
0.1%
690 3
< 0.1%
592 1
 
< 0.1%
382 2
 
< 0.1%
381 1
 
< 0.1%
332 1
 
< 0.1%
287 2
 
< 0.1%

preco
Real number (ℝ)

High correlation 

Distinct3211
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119912.51
Minimum2473
Maximum2040043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-18T16:46:50.374258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2473
5-th percentile50983.95
Q179805
median97778
Q3126514
95-th percentile245366
Maximum2040043
Range2037570
Interquartile range (IQR)46709

Descriptive statistics

Standard deviation105469.7
Coefficient of variation (CV)0.87955541
Kurtosis85.441486
Mean119912.51
Median Absolute Deviation (MAD)21812
Skewness7.4321792
Sum1.1991251 × 109
Variance1.1123857 × 1010
MonotonicityNot monotonic
2025-12-18T16:46:50.437939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87819 142
 
1.4%
106667 121
 
1.2%
100741 120
 
1.2%
94815 117
 
1.2%
81965 105
 
1.1%
96000 89
 
0.9%
97778 87
 
0.9%
84892 78
 
0.8%
90747 76
 
0.8%
78000 68
 
0.7%
Other values (3201) 8997
90.0%
ValueCountFrequency (%)
2473 1
 
< 0.1%
3229 1
 
< 0.1%
3370 1
 
< 0.1%
3516 1
 
< 0.1%
3821 7
0.1%
4102 1
 
< 0.1%
4454 1
 
< 0.1%
4688 1
 
< 0.1%
4729 1
 
< 0.1%
4834 1
 
< 0.1%
ValueCountFrequency (%)
2040043 1
< 0.1%
2014957 1
< 0.1%
1889572 1
< 0.1%
1784480 1
< 0.1%
1666873 1
< 0.1%
1657017 1
< 0.1%
1642532 1
< 0.1%
1610361 1
< 0.1%
1520000 1
< 0.1%
1485800 1
< 0.1%

valor_de_avaliacao
Real number (ℝ)

High correlation 

Distinct1852
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195617.89
Minimum16000
Maximum4000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-18T16:46:50.485322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum16000
5-th percentile90000
Q1135000
median163000
Q3205000
95-th percentile375000
Maximum4000000
Range3984000
Interquartile range (IQR)70000

Descriptive statistics

Standard deviation167485.24
Coefficient of variation (CV)0.85618569
Kurtosis119.62116
Mean195617.89
Median Absolute Deviation (MAD)33000
Skewness8.7361824
Sum1.9561789 × 109
Variance2.8051305 × 1010
MonotonicityNot monotonic
2025-12-18T16:46:50.548965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150000 265
 
2.6%
180000 234
 
2.3%
160000 232
 
2.3%
170000 230
 
2.3%
140000 204
 
2.0%
165000 177
 
1.8%
145000 171
 
1.7%
130000 164
 
1.6%
155000 147
 
1.5%
190000 137
 
1.4%
Other values (1842) 8039
80.4%
ValueCountFrequency (%)
16000 1
 
< 0.1%
18000 1
 
< 0.1%
20000 1
 
< 0.1%
21500 1
 
< 0.1%
25000 5
0.1%
27000 1
 
< 0.1%
27500 2
 
< 0.1%
28000 1
 
< 0.1%
28500 2
 
< 0.1%
29400 1
 
< 0.1%
ValueCountFrequency (%)
4000000 1
< 0.1%
3309000 1
< 0.1%
3201000 1
< 0.1%
3086000 1
< 0.1%
2960000 1
< 0.1%
2939000 1
< 0.1%
2800000 1
< 0.1%
2682000 1
< 0.1%
2630000 1
< 0.1%
2600000 2
< 0.1%

desconto
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.7008
Minimum10
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-18T16:46:50.596374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile32
Q138
median40
Q341
95-th percentile45
Maximum94
Range84
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.0235847
Coefficient of variation (CV)0.12653611
Kurtosis41.20243
Mean39.7008
Median Absolute Deviation (MAD)1
Skewness3.0549111
Sum397008
Variance25.236403
MonotonicityNot monotonic
2025-12-18T16:46:50.628065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
40 2705
27.1%
41 1934
19.3%
35 739
 
7.4%
36 732
 
7.3%
42 720
 
7.2%
45 705
 
7.0%
43 677
 
6.8%
39 500
 
5.0%
37 379
 
3.8%
30 295
 
2.9%
Other values (30) 614
 
6.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
15 6
 
0.1%
20 51
 
0.5%
25 83
 
0.8%
26 6
 
0.1%
27 45
 
0.4%
29 3
 
< 0.1%
30 295
2.9%
31 7
 
0.1%
32 11
 
0.1%
ValueCountFrequency (%)
94 27
0.3%
90 3
 
< 0.1%
74 1
 
< 0.1%
67 1
 
< 0.1%
65 2
 
< 0.1%
64 1
 
< 0.1%
62 5
 
0.1%
60 4
 
< 0.1%
57 1
 
< 0.1%
56 2
 
< 0.1%

area_total
Real number (ℝ)

High correlation  Zeros 

Distinct215
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.454
Minimum0
Maximum835
Zeros6295
Zeros (%)62.9%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-18T16:46:50.675416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q357
95-th percentile103
Maximum835
Range835
Interquartile range (IQR)57

Descriptive statistics

Standard deviation44.695884
Coefficient of variation (CV)1.570812
Kurtosis21.992969
Mean28.454
Median Absolute Deviation (MAD)0
Skewness2.8010388
Sum284540
Variance1997.7221
MonotonicityNot monotonic
2025-12-18T16:46:50.724478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6295
62.9%
45 112
 
1.1%
61 95
 
0.9%
46 95
 
0.9%
70 85
 
0.9%
48 81
 
0.8%
59 80
 
0.8%
62 78
 
0.8%
50 71
 
0.7%
44 71
 
0.7%
Other values (205) 2937
29.4%
ValueCountFrequency (%)
0 6295
62.9%
23 2
 
< 0.1%
25 1
 
< 0.1%
26 1
 
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
29 2
 
< 0.1%
30 4
 
< 0.1%
31 8
 
0.1%
32 3
 
< 0.1%
ValueCountFrequency (%)
835 1
< 0.1%
640 1
< 0.1%
527 1
< 0.1%
481 1
< 0.1%
462 1
< 0.1%
396 1
< 0.1%
381 1
< 0.1%
378 1
< 0.1%
371 1
< 0.1%
360 1
< 0.1%

area_privativa
Real number (ℝ)

High correlation  Zeros 

Distinct274
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.736
Minimum0
Maximum2125
Zeros579
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-18T16:46:50.786546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q142
median46
Q358
95-th percentile105
Maximum2125
Range2125
Interquartile range (IQR)16

Descriptive statistics

Standard deviation48.499205
Coefficient of variation (CV)0.88605681
Kurtosis473.19638
Mean54.736
Median Absolute Deviation (MAD)6
Skewness15.243198
Sum547360
Variance2352.1729
MonotonicityNot monotonic
2025-12-18T16:46:50.849913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 603
 
6.0%
43 587
 
5.9%
0 579
 
5.8%
44 561
 
5.6%
45 537
 
5.4%
41 459
 
4.6%
40 412
 
4.1%
46 394
 
3.9%
39 375
 
3.8%
47 364
 
3.6%
Other values (264) 5129
51.3%
ValueCountFrequency (%)
0 579
5.8%
4 3
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
12 1
 
< 0.1%
17 2
 
< 0.1%
18 3
 
< 0.1%
19 1
 
< 0.1%
20 2
 
< 0.1%
21 1
 
< 0.1%
ValueCountFrequency (%)
2125 1
< 0.1%
1500 1
< 0.1%
1189 1
< 0.1%
820 1
< 0.1%
747 1
< 0.1%
736 1
< 0.1%
656 1
< 0.1%
653 1
< 0.1%
640 1
< 0.1%
617 1
< 0.1%

area_terreno
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct553
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.1753
Minimum0
Maximum150000
Zeros6413
Zeros (%)64.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-18T16:46:50.881534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3140
95-th percentile350
Maximum150000
Range150000
Interquartile range (IQR)140

Descriptive statistics

Standard deviation2685.9595
Coefficient of variation (CV)11.045363
Kurtosis1291.6825
Mean243.1753
Median Absolute Deviation (MAD)0
Skewness30.202649
Sum2431753
Variance7214378.4
MonotonicityNot monotonic
2025-12-18T16:46:50.944776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6413
64.1%
200 336
 
3.4%
150 161
 
1.6%
160 159
 
1.6%
125 145
 
1.5%
180 121
 
1.2%
360 104
 
1.0%
250 99
 
1.0%
300 83
 
0.8%
126 60
 
0.6%
Other values (543) 2319
 
23.2%
ValueCountFrequency (%)
0 6413
64.1%
1 6
 
0.1%
2 1
 
< 0.1%
16 1
 
< 0.1%
28 1
 
< 0.1%
31 1
 
< 0.1%
35 5
 
0.1%
36 2
 
< 0.1%
37 2
 
< 0.1%
38 1
 
< 0.1%
ValueCountFrequency (%)
150000 1
< 0.1%
101622 1
< 0.1%
64039 1
< 0.1%
63050 1
< 0.1%
48040 1
< 0.1%
44586 2
< 0.1%
38063 1
< 0.1%
38001 1
< 0.1%
36841 1
< 0.1%
36502 1
< 0.1%

quartos
Real number (ℝ)

Missing 

Distinct8
Distinct (%)0.1%
Missing1529
Missing (%)15.3%
Infinite0
Infinite (%)0.0%
Mean2.0855861
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-18T16:46:50.976399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.43367
Coefficient of variation (CV)0.20793675
Kurtosis17.821917
Mean2.0855861
Median Absolute Deviation (MAD)0
Skewness2.1091477
Sum17667
Variance0.18806967
MonotonicityNot monotonic
2025-12-18T16:46:51.008232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 7148
71.5%
3 915
 
9.2%
1 342
 
3.4%
4 54
 
0.5%
5 9
 
0.1%
8 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
(Missing) 1529
 
15.3%
ValueCountFrequency (%)
1 342
 
3.4%
2 7148
71.5%
3 915
 
9.2%
4 54
 
0.5%
5 9
 
0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 1
 
< 0.1%
6 1
 
< 0.1%
5 9
 
0.1%
4 54
 
0.5%
3 915
 
9.2%
2 7148
71.5%
1 342
 
3.4%

salas
Categorical

Imbalance  Missing 

Distinct5
Distinct (%)0.1%
Missing1730
Missing (%)17.3%
Memory size514.7 KiB
1.0
8081 
2.0
 
173
3.0
 
12
6.0
 
2
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24810
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 8081
80.8%
2.0 173
 
1.7%
3.0 12
 
0.1%
6.0 2
 
< 0.1%
4.0 2
 
< 0.1%
(Missing) 1730
 
17.3%

Length

2025-12-18T16:46:51.040195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T16:46:51.071775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8081
97.7%
2.0 173
 
2.1%
3.0 12
 
0.1%
6.0 2
 
< 0.1%
4.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 8270
33.3%
0 8270
33.3%
1 8081
32.6%
2 173
 
0.7%
3 12
 
< 0.1%
6 2
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 8270
33.3%
0 8270
33.3%
1 8081
32.6%
2 173
 
0.7%
3 12
 
< 0.1%
6 2
 
< 0.1%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 8270
33.3%
0 8270
33.3%
1 8081
32.6%
2 173
 
0.7%
3 12
 
< 0.1%
6 2
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 8270
33.3%
0 8270
33.3%
1 8081
32.6%
2 173
 
0.7%
3 12
 
< 0.1%
6 2
 
< 0.1%
4 2
 
< 0.1%

vagas_garagem
Real number (ℝ)

Missing 

Distinct7
Distinct (%)0.1%
Missing3403
Missing (%)34.0%
Infinite0
Infinite (%)0.0%
Mean1.0665454
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-18T16:46:51.104455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31483113
Coefficient of variation (CV)0.29518775
Kurtosis167.1205
Mean1.0665454
Median Absolute Deviation (MAD)0
Skewness9.3334152
Sum7036
Variance0.099118643
MonotonicityNot monotonic
2025-12-18T16:46:51.135319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 6217
62.2%
2 347
 
3.5%
3 21
 
0.2%
4 8
 
0.1%
6 2
 
< 0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 3403
34.0%
ValueCountFrequency (%)
1 6217
62.2%
2 347
 
3.5%
3 21
 
0.2%
4 8
 
0.1%
6 2
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 1
 
< 0.1%
6 2
 
< 0.1%
4 8
 
0.1%
3 21
 
0.2%
2 347
 
3.5%
1 6217
62.2%

lavabos
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)1.3%
Missing9773
Missing (%)97.7%
Memory size546.1 KiB
1.0
222 
2.0
 
4
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters681
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 222
 
2.2%
2.0 4
 
< 0.1%
3.0 1
 
< 0.1%
(Missing) 9773
97.7%

Length

2025-12-18T16:46:51.182756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T16:46:51.198567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 222
97.8%
2.0 4
 
1.8%
3.0 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
. 227
33.3%
0 227
33.3%
1 222
32.6%
2 4
 
0.6%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 681
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 227
33.3%
0 227
33.3%
1 222
32.6%
2 4
 
0.6%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 681
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 227
33.3%
0 227
33.3%
1 222
32.6%
2 4
 
0.6%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 681
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 227
33.3%
0 227
33.3%
1 222
32.6%
2 4
 
0.6%
3 1
 
0.1%

suites
Unsupported

Missing  Rejected  Unsupported 

Missing10000
Missing (%)100.0%
Memory size78.3 KiB

cozinhas
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing1684
Missing (%)16.8%
Memory size514.5 KiB
1.0
8316 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24948
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 8316
83.2%
(Missing) 1684
 
16.8%

Length

2025-12-18T16:46:51.230193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T16:46:51.246598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8316
100.0%

Most occurring characters

ValueCountFrequency (%)
1 8316
33.3%
. 8316
33.3%
0 8316
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24948
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8316
33.3%
. 8316
33.3%
0 8316
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24948
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8316
33.3%
. 8316
33.3%
0 8316
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24948
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8316
33.3%
. 8316
33.3%
0 8316
33.3%

varandas
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.2%
Missing9483
Missing (%)94.8%
Memory size545.0 KiB
1.0
517 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1551
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 517
 
5.2%
(Missing) 9483
94.8%

Length

2025-12-18T16:46:51.278044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T16:46:51.309715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 517
100.0%

Most occurring characters

ValueCountFrequency (%)
1 517
33.3%
. 517
33.3%
0 517
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1551
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 517
33.3%
. 517
33.3%
0 517
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1551
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 517
33.3%
. 517
33.3%
0 517
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1551
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 517
33.3%
. 517
33.3%
0 517
33.3%

sacadas
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.6%
Missing9824
Missing (%)98.2%
Memory size546.3 KiB
1.0
176 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters528
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 176
 
1.8%
(Missing) 9824
98.2%

Length

2025-12-18T16:46:51.341349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T16:46:51.373038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 176
100.0%

Most occurring characters

ValueCountFrequency (%)
1 176
33.3%
. 176
33.3%
0 176
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 176
33.3%
. 176
33.3%
0 176
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 176
33.3%
. 176
33.3%
0 176
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 176
33.3%
. 176
33.3%
0 176
33.3%

terracos
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.4%
Missing9751
Missing (%)97.5%
Memory size546.0 KiB
1.0
249 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters747
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 249
 
2.5%
(Missing) 9751
97.5%

Length

2025-12-18T16:46:51.579013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T16:46:51.594868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 249
100.0%

Most occurring characters

ValueCountFrequency (%)
1 249
33.3%
. 249
33.3%
0 249
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 747
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 249
33.3%
. 249
33.3%
0 249
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 747
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 249
33.3%
. 249
33.3%
0 249
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 747
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 249
33.3%
. 249
33.3%
0 249
33.3%

areas_servico
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing5593
Missing (%)55.9%
Memory size529.8 KiB
1.0
4407 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13221
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 4407
44.1%
(Missing) 5593
55.9%

Length

2025-12-18T16:46:51.627149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T16:46:51.658792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4407
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4407
33.3%
. 4407
33.3%
0 4407
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13221
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4407
33.3%
. 4407
33.3%
0 4407
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13221
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4407
33.3%
. 4407
33.3%
0 4407
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13221
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4407
33.3%
. 4407
33.3%
0 4407
33.3%

dce
Categorical

Constant  Missing 

Distinct1
Distinct (%)1.1%
Missing9905
Missing (%)99.1%
Memory size546.6 KiB
1.0
95 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters285
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 95
 
0.9%
(Missing) 9905
99.1%

Length

2025-12-18T16:46:51.674579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T16:46:51.707583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 95
100.0%

Most occurring characters

ValueCountFrequency (%)
1 95
33.3%
. 95
33.3%
0 95
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 285
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 95
33.3%
. 95
33.3%
0 95
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 285
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 95
33.3%
. 95
33.3%
0 95
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 285
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 95
33.3%
. 95
33.3%
0 95
33.3%

churrasqueiras
Categorical

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing9998
Missing (%)> 99.9%
Memory size547.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
(Missing) 9998
> 99.9%

Length

2025-12-18T16:46:51.738007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T16:46:51.753792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

wc
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing1811
Missing (%)18.1%
Memory size515.0 KiB
1.0
8189 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24567
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 8189
81.9%
(Missing) 1811
 
18.1%

Length

2025-12-18T16:46:51.785454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T16:46:51.827557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8189
100.0%

Most occurring characters

ValueCountFrequency (%)
1 8189
33.3%
. 8189
33.3%
0 8189
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24567
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8189
33.3%
. 8189
33.3%
0 8189
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24567
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8189
33.3%
. 8189
33.3%
0 8189
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24567
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8189
33.3%
. 8189
33.3%
0 8189
33.3%

wc_emp
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.6%
Missing9830
Missing (%)98.3%
Memory size546.3 KiB
1.0
170 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters510
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 170
 
1.7%
(Missing) 9830
98.3%

Length

2025-12-18T16:46:51.862895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T16:46:51.886458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 170
100.0%

Most occurring characters

ValueCountFrequency (%)
1 170
33.3%
. 170
33.3%
0 170
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 510
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 170
33.3%
. 170
33.3%
0 170
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 510
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 170
33.3%
. 170
33.3%
0 170
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 510
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 170
33.3%
. 170
33.3%
0 170
33.3%

m2total
Real number (ℝ)

High correlation  Zeros 

Distinct1786
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean608.363
Minimum0
Maximum8498
Zeros6295
Zeros (%)62.9%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-18T16:46:51.916033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31275
95-th percentile2356.05
Maximum8498
Range8498
Interquartile range (IQR)1275

Descriptive statistics

Standard deviation895.58175
Coefficient of variation (CV)1.4721174
Kurtosis1.4377637
Mean608.363
Median Absolute Deviation (MAD)0
Skewness1.3295554
Sum6083630
Variance802066.68
MonotonicityNot monotonic
2025-12-18T16:46:51.975437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6295
62.9%
1439 9
 
0.1%
1529 9
 
0.1%
1830 9
 
0.1%
1481 9
 
0.1%
1707 9
 
0.1%
1123 9
 
0.1%
1477 8
 
0.1%
1349 8
 
0.1%
2239 8
 
0.1%
Other values (1776) 3627
36.3%
ValueCountFrequency (%)
0 6295
62.9%
184 1
 
< 0.1%
216 1
 
< 0.1%
220 1
 
< 0.1%
232 1
 
< 0.1%
244 1
 
< 0.1%
286 1
 
< 0.1%
308 1
 
< 0.1%
318 1
 
< 0.1%
328 1
 
< 0.1%
ValueCountFrequency (%)
8498 1
< 0.1%
6028 1
< 0.1%
5883 1
< 0.1%
5219 1
< 0.1%
4947 1
< 0.1%
4880 1
< 0.1%
4711 1
< 0.1%
4419 1
< 0.1%
4358 1
< 0.1%
4345 1
< 0.1%

m2privativa
Real number (ℝ)

High correlation  Zeros 

Distinct3039
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2056.7122
Minimum0
Maximum25926
Zeros579
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-18T16:46:52.027851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11506.75
median2015
Q32511.25
95-th percentile3735.1
Maximum25926
Range25926
Interquartile range (IQR)1004.5

Descriptive statistics

Standard deviation1126.4569
Coefficient of variation (CV)0.54769789
Kurtosis57.793927
Mean2056.7122
Median Absolute Deviation (MAD)503
Skewness3.7569324
Sum20567122
Variance1268905.2
MonotonicityNot monotonic
2025-12-18T16:46:52.091804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 579
 
5.8%
1996 29
 
0.3%
1800 26
 
0.3%
2370 26
 
0.3%
2000 25
 
0.2%
3022 24
 
0.2%
2091 22
 
0.2%
1952 21
 
0.2%
1744 21
 
0.2%
2177 20
 
0.2%
Other values (3029) 9207
92.1%
ValueCountFrequency (%)
0 579
5.8%
52 1
 
< 0.1%
56 1
 
< 0.1%
66 1
 
< 0.1%
69 1
 
< 0.1%
73 1
 
< 0.1%
78 1
 
< 0.1%
79 1
 
< 0.1%
88 1
 
< 0.1%
100 1
 
< 0.1%
ValueCountFrequency (%)
25926 1
< 0.1%
24889 1
< 0.1%
22357 1
< 0.1%
17054 1
< 0.1%
14182 1
< 0.1%
13571 1
< 0.1%
12479 1
< 0.1%
11929 1
< 0.1%
11886 1
< 0.1%
11479 1
< 0.1%

m2terreno
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct1344
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean338.0823
Minimum0
Maximum202826
Zeros6413
Zeros (%)64.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-18T16:46:52.175995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3367
95-th percentile1099.15
Maximum202826
Range202826
Interquartile range (IQR)367

Descriptive statistics

Standard deviation3944.5115
Coefficient of variation (CV)11.667311
Kurtosis1915.8578
Mean338.0823
Median Absolute Deviation (MAD)0
Skewness42.336763
Sum3380823
Variance15559171
MonotonicityNot monotonic
2025-12-18T16:46:52.225371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6413
64.1%
2 22
 
0.2%
585 13
 
0.1%
363 12
 
0.1%
430 12
 
0.1%
510 12
 
0.1%
405 11
 
0.1%
398 10
 
0.1%
549 10
 
0.1%
236 10
 
0.1%
Other values (1334) 3475
34.8%
ValueCountFrequency (%)
0 6413
64.1%
1 4
 
< 0.1%
2 22
 
0.2%
3 10
 
0.1%
4 9
 
0.1%
5 3
 
< 0.1%
6 8
 
0.1%
7 5
 
0.1%
8 5
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
202826 1
< 0.1%
197567 1
< 0.1%
171445 1
< 0.1%
139015 1
< 0.1%
101432 1
< 0.1%
98134 1
< 0.1%
70938 1
< 0.1%
11988 1
< 0.1%
5736 1
< 0.1%
5240 1
< 0.1%

Interactions

2025-12-18T16:46:47.536843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:25.549192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:26.781417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:28.171942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:29.453998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:30.773909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:31.990508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:33.308943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:34.549945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:35.886628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:37.268424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:38.454633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:39.633522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:40.817501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:42.290501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:43.586531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:44.873329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:46.272279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:47.623948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:25.600097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:26.838408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:28.255518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:29.515033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:30.839036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:32.056171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:33.382941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:34.614877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:35.956210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:37.353999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:38.516382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:39.704518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:40.870478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:42.375975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:43.649259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:44.932034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:46.332714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:47.706621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:25.676531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:26.886253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:28.335069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:29.596255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:30.915980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:32.124039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:33.457167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:34.690007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:36.020121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:37.405798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:38.591182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:39.756035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:40.950127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:42.452628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:43.738716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:45.021067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:46.404025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:47.770602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:25.755831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:26.949401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:28.392565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:29.690521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:30.948450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:32.189231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:33.533602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:34.768679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:36.083910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:37.449823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:38.649753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-18T16:46:44.589980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:46.016806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:47.249933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:48.649745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:26.572199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:27.748430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:29.234773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:30.526608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:31.779193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:33.149299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:34.283674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:35.671929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:37.055583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:38.252065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:39.434183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:40.640344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:42.072833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:43.402483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:44.655258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:46.070300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:47.335064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:48.704566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:26.633729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:27.823040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:29.300145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:30.604571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:31.857422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:33.206938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:34.388950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:35.743109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:37.136519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:38.316133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:39.482868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:40.686389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:42.129458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:43.484312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:44.737215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:46.140254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:47.384742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:48.748789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:26.721383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:28.069465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:29.386121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:30.683031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:31.917176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:33.263375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:34.466691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:35.816829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:37.187898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:38.397280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:39.548614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:40.756491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:42.202979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:43.532613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:44.799845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:46.218141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T16:46:47.457675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-18T16:46:52.293138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
area_privativaarea_terrenoarea_totalclassificacaodescontolavabosm2privativam2terrenom2totaln_lancesperc_de_lutaprecoquartosrow_idsalasvagas_garagemvalor_de_avaliacaovalor_ofertavalor_presentevalor_venda
area_privativa1.0000.2370.044-0.016-0.2140.718-0.1440.319-0.025-0.085-0.0450.2860.466-0.1140.3170.2180.2830.2730.2730.286
area_terreno0.2371.000-0.0830.0200.0591.000-0.4910.901-0.069-0.0820.004-0.1140.200-0.0550.0000.164-0.109-0.088-0.088-0.114
area_total0.044-0.0831.0000.0480.0140.0000.135-0.0430.897-0.010-0.0500.1210.0250.0280.172-0.0060.1310.0930.0930.121
classificacao-0.0160.0200.0481.0000.0030.0000.0140.0160.053-0.0330.0330.010-0.0210.0090.0000.0170.0110.0180.0180.010
desconto-0.2140.0590.0140.0031.0000.060-0.4420.015-0.0050.0980.064-0.678-0.215-0.0590.277-0.147-0.600-0.619-0.619-0.679
lavabos0.7181.0000.0000.0000.0601.0000.0001.0000.0001.0000.0000.5090.1420.0690.4240.4010.3770.5370.5370.509
m2privativa-0.144-0.4910.1350.014-0.4420.0001.000-0.3880.225-0.030-0.0590.683-0.1170.2220.0000.0710.6770.6060.6060.683
m2terreno0.3190.901-0.0430.0160.0151.000-0.3881.000-0.014-0.059-0.035-0.0250.186-0.0460.0000.156-0.021-0.016-0.016-0.025
m2total-0.025-0.0690.8970.053-0.0050.0000.225-0.0141.000-0.011-0.0510.160-0.0540.0720.056-0.0100.1710.1280.1280.160
n_lances-0.085-0.082-0.010-0.0330.0981.000-0.030-0.059-0.0111.000-0.088-0.144-0.075-0.0710.000-0.063-0.147-0.184-0.184-0.144
perc_de_luta-0.0450.004-0.0500.0330.0640.000-0.059-0.035-0.051-0.0881.000-0.0720.004-0.0240.0610.002-0.0620.2730.273-0.072
preco0.286-0.1140.1210.010-0.6780.5090.683-0.0250.160-0.144-0.0721.0000.2230.1610.1600.1940.9880.9070.9071.000
quartos0.4660.2000.025-0.021-0.2150.142-0.1170.186-0.054-0.0750.0040.2231.000-0.1120.4760.2260.2180.2290.2290.223
row_id-0.114-0.0550.0280.009-0.0590.0690.222-0.0460.072-0.071-0.0240.161-0.1121.0000.0000.0090.1700.1390.1390.161
salas0.3170.0000.1720.0000.2770.4240.0000.0000.0560.0000.0610.1600.4760.0001.0000.4070.1990.1430.1430.160
vagas_garagem0.2180.164-0.0060.017-0.1470.4010.0710.156-0.010-0.0630.0020.1940.2260.0090.4071.0000.1940.1980.1970.194
valor_de_avaliacao0.283-0.1090.1310.011-0.6000.3770.677-0.0210.171-0.147-0.0620.9880.2180.1700.1990.1941.0000.9030.9030.988
valor_oferta0.273-0.0880.0930.018-0.6190.5370.606-0.0160.128-0.1840.2730.9070.2290.1390.1430.1980.9031.0001.0000.907
valor_presente0.273-0.0880.0930.018-0.6190.5370.606-0.0160.128-0.1840.2730.9070.2290.1390.1430.1970.9031.0001.0000.907
valor_venda0.286-0.1140.1210.010-0.6790.5090.683-0.0250.160-0.144-0.0721.0000.2230.1610.1600.1940.9880.9070.9071.000

Missing values

2025-12-18T16:46:48.899038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-18T16:46:49.021819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-18T16:46:49.182703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

classificacaovalor_vendavalor_ofertavalor_presenterow_idperc_de_lutan_lancesprecovalor_de_avaliacaodescontoarea_totalarea_privativaarea_terrenoquartossalasvagas_garagemlavabossuitescozinhasvarandassacadasterracosareas_servicodcechurrasqueiraswcwc_empm2totalm2privativam2terreno
01.0225400.00321400.00321400.00743.023225400.0322000.030.00.070.00.03.01.01.0NaNNaN1.0NaNNaNNaN1.0NaNNaN1.0NaN0.03220.00.0
11.0370600.00370600.00370600.001470.02370600.0436000.015.00.074.00.03.01.01.0NaNNaN1.0NaNNaNNaNNaNNaNNaNNaN1.00.05008.00.0
21.0264000.00264000.00264000.001070.01264000.0330000.020.00.0146.0189.0NaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.01808.01397.0
31.0242008.72242008.72242008.72430.012242009.0302511.020.00.032.00.01.01.0NaNNaNNaN1.0NaNNaNNaNNaNNaNNaN1.0NaN0.07563.00.0
41.0154522.77166522.77166522.77398.01154523.0206030.025.00.00.0480.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0322.0
51.0156978.00176978.00176978.009513.02156978.0243000.035.00.064.00.03.01.01.0NaNNaN1.0NaNNaNNaNNaNNaNNaN1.0NaN0.02453.00.0
61.0163200.00195200.00195200.008320.05163200.0217600.025.00.060.0130.02.01.02.0NaNNaN1.0NaNNaNNaNNaNNaNNaN1.0NaN0.02720.01255.0
71.0212000.00248000.00248000.007517.013212000.0265000.020.00.069.00.02.01.01.0NaNNaN1.0NaNNaNNaNNaNNaNNaN1.0NaN0.03072.00.0
81.088200.0088200.0088200.00990.0688200.0126000.030.00.057.0200.0NaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.01547.0441.0
91.0119000.00151000.00151000.003127.01119000.0170000.030.00.049.00.02.01.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaN0.02429.00.0
classificacaovalor_vendavalor_ofertavalor_presenterow_idperc_de_lutan_lancesprecovalor_de_avaliacaodescontoarea_totalarea_privativaarea_terrenoquartossalasvagas_garagemlavabossuitescozinhasvarandassacadasterracosareas_servicodcechurrasqueiraswcwc_empm2totalm2privativam2terreno
99901.0100148.49134148.49134148.498434.01100148.0169000.041.053.043.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1890.02329.00.0
99911.0137010.64149010.64149010.644529.01137011.0217676.037.040.028.00.01.01.0NaNNaNNaN1.0NaNNaNNaNNaNNaNNaN1.0NaN3425.04893.00.0
99921.0155402.67155402.67155402.674350.01155403.0253800.039.00.052.00.0NaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.02989.00.0
99931.068787.97127787.97127787.9729486.0168788.0120000.043.068.045.00.02.01.01.0NaNNaN1.0NaNNaNNaNNaNNaNNaN1.0NaN1012.01529.00.0
99941.0117860.63128860.63128860.632909.01117861.0195000.040.068.045.00.02.01.01.0NaNNaN1.0NaNNaNNaN1.0NaNNaN1.0NaN1733.02619.00.0
99951.078201.8788201.8788201.8735113.0978202.0135000.042.057.046.00.02.01.01.0NaNNaN1.0NaNNaNNaNNaNNaNNaN1.0NaN1372.01700.00.0
99961.0100741.08100741.08100741.081440.01100741.0170000.041.060.042.00.02.01.01.0NaNNaN1.0NaNNaNNaN1.0NaNNaN1.0NaN1679.02399.00.0
99971.0106963.33112963.33112963.334886.09106963.0180500.041.083.052.00.02.01.01.0NaNNaN1.0NaNNaNNaN1.0NaNNaN1.0NaN1289.02057.00.0
99981.094815.1494815.1494815.141480.01994815.0160000.041.00.042.00.02.01.01.0NaNNaN1.0NaNNaNNaN1.0NaNNaN1.0NaN0.02258.00.0
99991.0116047.39156047.39156047.398834.02116047.0192000.040.053.043.00.02.01.0NaNNaNNaN1.0NaNNaNNaNNaNNaNNaN1.0NaN2190.02699.00.0